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ICCV
2007
IEEE

Probabilistic Linear Discriminant Analysis for Inferences About Identity

10 years 1 days ago
Probabilistic Linear Discriminant Analysis for Inferences About Identity
Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both withinindividual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.
Simon J. D. Prince, James H. Elder
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where ICCV
Authors Simon J. D. Prince, James H. Elder
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